In 2023, I had had enough of hearing tech bros blaming unconscious bias for all the ways in which AI was weaponised against women. Decided to demonstrate intent, I wrote Techno-Patriarchy: How AI is Misogyny’s New Clothes, originally published in The Mint.
In the article, I detailed 12 ways this technology is used against women, from reinforcing stereotypes to pregnancy surveillance. One shocked me to my core: Non-consensual sexual synthetic imagery (aka “deepfake porn”).
It was completely horrifying, dehumanizing, degrading, violating to just see yourself being misrepresented and being misappropriated in that way.
It robs you of opportunities, and it robs you of your career, and your hopes and your dreams.
Noelle Martin, “deepfake porn” victim, award-winning activist, and law reform campaigner.
So I continued to write about the dire consequences of this technology for victims and the legal vacuum, as well as denounced the powerful ecosystem (tech, payment processors, marketplaces) that fostered and profited from them.
I also made a point to bring awareness about how this technology is harming women and girls in spaces where the topic of “deepfakes” was explored broadly. I organised events, appeared on podcasts, and participated in panels, such as “The Rise of Deepfake AI” at the University of Oxford; all opportunities were fair game to bring “deepfake porn” to the forefront.
This week, I had 30 minutes to convince over 80 women in tech – and allies – to become advocates against non-consensual sexual synthetic imagery. The feedback I received from the keynote was very positive, so I’m sharing my talking points with you below.
I hope that by the end of the article, (a) you are convinced that we need to act now, and (b) you have decided how you will help to advocate against this pandemic.
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The State of Play
All that’s wrong with using the term “deepfake porn”
I had an aha moment when I realised the disservice the term “deepfake porn” was doing to addressing this issue.
“Deepfake” honours the name of the Reddit user who shared on the platform the first synthetic intimate media of actresses. When paired with the label “porn”, it may wrongly convey the idea that it’s consensual. Overall, the term lacks gravitas, disregarding harms.
From a legal perspective, the use of the term “deepfake” may also hinder the pursuit of justice. There have been cases where filing a lawsuit using the term deepfakes when referring to a “cheapfake” — which consists of a fake piece of media created with conventional methods of doctoring images rather than AI — has blocked prosecution.
In 2021, van Wynsberghe proposed defining sustainable artificial intelligence (AI) as “a movement to foster change in the entire lifecycle of AI products (i.e., idea generation, training, re-tuning, implementation, governance) towards greater ecological integrity and social justice”. The concept comprised two key contributions: AI for sustainability and the sustainability of AI.
At the time, a growing effort was already underway exploring how AI tools could help address climate change challenges (AI for sustainability). However, studies have already shown that developing large Natural Language Processing (NLP) AI models results in significant energy consumption and carbon emissions, often caused by using non-renewable energy. van Wynsberghe posited the need to focus on the sustainability of AI.
Four years later, the conversation about making AI sustainable has evolved considerably with the arrival of generative AI models. These models have popularised and democratised the use of artificial intelligence, especially as a productivity tool for generating content.
Another factor that has exponentially increased the resources dedicated to AI is the contested hypothesis that developing AI models with increasingly large datasets and algorithmic complexity will ultimately lead to Artificial General Intelligence (AGI) — a type of AI system that would match or surpass human cognitive capabilities.
Powerful businesses, governments, and academia consider AGI a competitive advantage. Tech leaders such as Eric Schmidt (former Google CEO) and Sam Altman (OpenAI CEO) have disregarded concerns about AI’s sustainability, as AGI will supposedly solve them in the future.
In this context, what do current trends reveal about the sustainability of AI?
Challenges
Typically, artificial intelligence models are developed and run on the cloud, which is powered by data centres. As a result, their construction has increased significantly over the past few years. McKinsey estimates that global demand for data centre capacity could rise between 19% and 22% annually from 2023 to 2030.
More than twenty years ago, I joined a medium size software company focused on scientific modelling as a trainer. I knew the company and some of their products very well. I had been their customer.
First, during my PhD in computational chemistry, then as an EU post-doctoral researcher coding FORTRAN subroutines to simulate the behaviour of materials, and as a modelling engineer working for a large chemical company.
As I started my job as a materials trainer, I had to learn about other software applications that I hadn’t used previously or was less familiar with. One of those was related to what we called at the time “statistics” to predict the properties of new materials.
Some of those “statistical methods” were neural networks and genetic algorithms, part of the field of artificial intelligence. But I was not keen on developing the material for that course. It felt like a waste of time for several reasons.
First, whilst those methods were already popular among life science researchers, they were not very helpful to materials modellers — my customers. Why? Because large, good datasets were scarce for materials.
Point in case, I still remember one specific customer excited about using the algorithms to develop new materials in their organisation. With a sinking feeling from similar conversations, I asked him, “How many data points do you have?”. He said, “I think I have 7 or 10 in a spreadsheet.” Unfortunately, I had to inform him that it was not nearly enough.
Second, the course was half a day, which was not practical to be delivered in person, the way all our workshops had been offered for years. Our experience told us that in 2005, nobody would fly to Paris, Cambridge, Boston, or San Diego for a 4-hour training event on “statistics”.
The solution? It was decided that this course would be the first to be delivered online via a “WebEx”, the great-grandparent of Zoom, Teams, and Google Meet. That was not cool at all.
At the time, we had little faith in online education for three reasons.
Running the webinars was very complex; they took ages to set up and schedule, and there were always connection glitches.
There were no “best practices” to deliver engaging online training yet, as a result, we trainers felt as if we were cheating on our job to teach our clients.
We believed that scientific and technical content was “unteachable” online.
After such a less-than-amazing start at teaching artificial intelligence online, you’d have thought I was done.
I thought so, too. But I’ve changed my mind. It hasn’t happened overnight, though.
It has taken two decades of experience teaching, using, and supporting AI tools in my corporate job, 10+ years as a DEI trailblazer, and my activism for sustainable AI for the last four years to realise that if we want systemic equality, it’s paramount we bridge the gender gap in AI adoption.
And it has also helped that I now have 20 years of experience delivering engaging online keynotes, courses, and masterclasses.
After training, I moved to the Contract Research department. There, I had the opportunity to design and deliver projects that used AI algorithms to get insights into new materials and their properties.
Later on, I became Head of Training and Contract Research and afterwards, I moved to supporting customers using our software applications for both materials and life sciences research.
Whilst there were exciting developments in those areas, most of our AI algorithms didn’t get much love from our developers or customers. After all, they hadn’t substantially improved for ages.
Then, all changed a few years ago.
In life science, AI algorithms made it possible to predict protein structure, which earned their creators the Nobel Prize. Those models have been used in pharmaceuticals and environmental technology research and were available to our customers.
We also developed applications that used AI algorithms to help accelerate drug discovery. It was hearing from clients working on cancer treatments how AI has positively broadened the kind of drugs they were considering that changed me from AI-neutral to AI-positive.
In materials science, machine learning forcefiels are also bridging the gap between quantum and classical simulation, making it possible to simultaneously model chemical reactions (quantum) in relatively large systems (classical).
In summary, my corporate job taught me that scientific research can benefit massively from the development of AI tools beyond ChatGPT.
As a DEI Trailblazer
Tired of tech applications that made users vulnerable and denied their diversity of experiences, in 2019, I launched the Ethics and Inclusion Framework.
The idea was simple — a free tool for tech developers to help them identify, prevent, mitigate, and account for the actual and potential adverse impact of the solution they develop. The approach is general so that it can be used for any software applications, including AI tools.
It was running a workshop on the framework that I met Tania Duarte, the founder of We and AI, an NGO working to encourage, enable, and empower critical thinking about AI.
I joined them in 2020 and it has been a joy to contribute to initiatives such as
The Race and AI Toolkit, designed to raise awareness of how AI algorithms encode and amplify the racial biases in our society.
Better Images of AI, a thought-provoking library of free images that more realistically portray AI and the people behind it, highlighting its strengths, weaknesses, context, and applications.
Living with AI, the e-learning course of the Scottish AI Alliance.
Additionally, as a founder of the gender employee community at my corporate job a decade ago, I’ve chaired multiple insightful meetings where we’ve discussed the impact of AI algorithms on diversity, equity, and inclusion.
I was on a mission to make others aware, too. I still remember my keynote at the Dassault Systèmes Sustainability Townhall in 2021, when I shared with my co-workers the urgency to think about the materiality of AI — you can watch here a shorter version I delivered at the WomenTech Conference in 2022.
I’ve also written about how the Global North exploits the Global South’s mineral resources to power AI, as well as how tech companies and governments disregard the energy and water consumption from running generative AI tools.
Lately, I’ve looked into data centres — which are vital to cloud services and hence to the development and deployment of AI. Given that McKinsey forecasts that they’ll triple in number by 2030, it’s paramount that we balance innovation and environmental responsibility.
AI and Women
As 50% of the population on the planet, women have been affected by AI developments, but typically not as the ones profiting from it, but instead bearing the brunt of it.
Women Leading AI
Unfortunately, it often appears that the only contribution from women to technology was made by Ada Lovelace, in the 19th century. Artificial intelligence is no exception. The contributions of women to AI have been regularly downplayed.
The article prompted criticism right away and “counter-lists” of women who have been pivotal in AI development and uncovering its harms. Still, women are not seen as “AI visionaries”.
And it’s not only society that disregards women’s expertise on AI — women themselves do that.
In 2023, I was collaborating with an NGO that focuses on increasing the number of women in leadership positions in fintech. They asked me to chair a panel at their annual conference and gave me freedom to pick the topic. I titled the panel “The role of boards driving AI adoption.”
In alignment with the mission of the NGO, we decided that we’d have one male and two females as panelists.
Finding a great male expert was fast. Finding the two female AI experts was long and excruciating.
And not because of the lack of talent. It was a lack of “enoughness.”
For three weeks, I met women who had solid experience working in teams developing and implementing strategies for AI tools. Still, they didn’t feel they were “expert enough” to be in the panel.
I finally got two smashing female AI experts but the search opened my mind to the need to get more women on boards to learn about AI tools as well as their impact on strategy and governance.
That was the rationale behind launching the Strategic AI Leadership Program, a bespoke course on AI Competence for C-Suite and Boards. The feedback was excellent and it filled me with pride to empower women in top leadership positions to have discussions about responsible and sustainable AI.
Syncophant chatbots can hide the fact that at its core, AI is a tool that automates and scales the past.
As such, it’s been consistently weaponised as a misogyny tool and its harms disregarded as unconscious bias and blamed on the lack of diversity of datasets.
And I’m not talking about “old” artificial intelligence, only. Generative AI is massively contributing to reinforcing harmful stereotypes and is being weaponised against women and underrepresented groups.
And chatbots are great enablers of propagating biases.
New research has found that ChatGPT and Claud consistently advise women to ask for lower salaries than men, even when both have identical qualifications.
In one example, ChatGPT’s o3 model was prompted to advise a female job applicant. The model suggested requesting a salary of $280,000. In another, the researchers made the same prompt but for a male applicant. This time, the model suggested a salary of $400,000.
In summary, not only does AI foster biases but it also helps promote them on a planetary scale.
My Aha Moment
Until recently, my focus had been to empower people with knowledge about how AI algorithms work, as well as AI strategy and governance. I had avoided teaching generative AI practices like the plague.
That was until a breakthrough through the month of July. It came as the convergence of four aspects.
Non-Tech Women
A month ago, I delivered the keynote “The Future of AI is Female” at the Women’s Leadership event Phoenix 2, hosted by Aspire.
In that session, I shared with the audience two futures: one where AI tools are used to transform us into “productive beings” and another one where AI systems are used to improve our health, enhance sustainability, and boost equity.
It’s a no-brainer that everybody thought the second scenario was better. But it was also very telling that nobody believed that it was the most probable.
After the keynote, many attendees reached out to me and asked for a course to learn how AI could be used for good and in alignment with their values.
Other women who didn’t attend the conference also reached out to me for guidance on AI courses to help them strengthen their professional profiles beyond “prompting”.
Unfortunately, I wasn’t able to recommend a course that incorporates both practical knowledge about AI and the fundamentals of how it shapes areas such as sustainability, DEI, strategy, and governance.
Women In Tech
As I mentioned above, I’m the founder of the gender employee community at my corporate job, and for 10 years, we’ve been hosting regular meetings to discuss DEI topics.
For our July meeting, I wanted us to have an uplifting session before the summer break, so I proposed to discuss how AI can boost DEI now and in the future.
I went to the meeting happily prepared with my list of examples of how artificial intelligence was supporting diversity, equity, and inclusion. But I was not prepared for how the session panned out.
Over and over, the examples shared showcased how AI was weaponised against DEI. Moreover, when a positive use was shared, somebody quickly pointed out how that could be used against underrepresented groups.
This experience made me realise that as well as thinking through the challenges, DEI advocates also need to spend time and be given the tools to think about how AI can purposefully drive equity.
Women In Ethics
I have the privilege of counting many women experts in ethical AI, with relevant academic background and professional experience.
With all the talk about responsible AI, you’d think that they are in high demand. They aren’t.
In July, my LinkedIn feed was full of posts from ethics experts — many of them women — complaining of what I call “performative AI ethics,” organisations praising the need to embed responsible AI without creating the necessary role.
But is that true? Yes, and no.
Looking at the advertised AI job, I noticed that the tendency is for expertise in ethics to appear as an add-on to “Head of AI” roles that are at the core eminently technical: Their key requirement is experience designing, deploying, and using AI tools.
In other words, technical expertise remains the gatekeeper to responsible AI.
As I mentioned in my recent article “A New Religion: 8 Signs AI Is Our New God”, it has been taken as a dogma that women are behind in generative AI adoption because of lower confidence in their ability to use AI tools effectively and lack of interest in this technology.
But a recent Harvard Business School working paper Global Evidence on Gender Gaps and Generative AI, synthesising data from 18 studies covering more than 140,000 individuals worldwide, has provided a much nuanced understanding of the gender divide in generative AI.
When compared to men, women are more likely to
Say they need training before they can benefit from ChatGPT compared to men and to perceive AI usage in coursework or assignments as unethical or equivalent to cheating.
Agree that chatbots should be prohibited in educational settings, and be more concerned about how generative AI will impact learning in the future.
Perceive lower productivity benefits of using generative AI at work and in job search.
Agree that chatbots can generate better results than they can on their own.
Moreover, women are less likely to agree that chatbots can improve their language ability or to trust generative AI than traditional human-operated services in education and training, information, banking, health, and public policy services.
In summary, women correctly understand that AI is not “neutral” or a religion to be blindly adopted and prefer not to use it when they perceive it as unethical.
There is more. In the HBR article Research: The Hidden Penalty of Using AI at Work, researchers reported an experiment with 1,026 engineers in which participants evaluated a code snippet that was purportedly written by another engineer, either with or without AI assistance. The code itself was the same — the only difference was the described method of creation (with/without AI assistance).
When reviewers believed an engineer had used AI, they rated that engineer’s competence 9% lower on average, with 6% for men and 13% for women.
When members of stereotyped groups — for example, women in tech or older workers in youth-dominated fields — use AI, it reinforces existing doubts about their competence. The AI assistance is framed as a “proof” of their inadequacy rather than evidence of their strategic tool use. Any industry predominated by one segment over another is likely to witness greater competence penalties on minority workers.
The authors offer senior women openly using AI as a solution to bridging the gap.
Our research found that women in senior roles were less afraid of the competence penalty than their junior counterparts. When these leaders openly use AI, they provide crucial cover for vulnerable colleagues.
A study by BCG also illustrates this dynamic: When senior women managers lead their male counterparts in AI adoption, the adoption gap between junior women and men shrinks significantly.
Basically, we need to normalise women using—and leading—AI.
My Bet: Women Leading with AI
Through my July of AI breakthroughs, I learned that
The gender gap in generative AI is real, and the causes are much more complex than a lack of confidence.
The absence of access to training and sustainable practices is a factor contributing to that gender gap.
Women are eager to ramp up on AI provided that it aligns with their values.
To be considered by organisations to lead responsible AI, it’s imperative to show mastery of the tools.
This coalesced in a bold idea:
What if I teach women how to use AI within an ethical, inclusive, and sustainable framework?
What if I developed a program where they can both understand how AI tools work, their impact on topics such as the future of work, DEI, strategy, and governance, while developing expertise on tools with practical examples?
A structured, eight-session program for women leaders focused on turning AI literacy into strategic results. Explore AI foundations and the impact of artificial intelligence on the future of work, DEI, sustainability, data and cybersecurity — paired with generative AI workflows, templates, exercises, and decision frameworks to translate learning into real-world impact. The blend of live instruction, quizzes, and peer support ensures you emerge with both critical insight and a toolkit ready to lead impactfully in your role.
The program starts mid-September and you can read the details following this link.
I can not wait for you to join me in making the future of AI female.
[Webinar Invitation] Ethical AI Leadership: Balancing Innovation, Inclusion & Sustainability
Join me on Tuesday, 12th August for a practical, high-value webinar tailored for women leaders committed to harnessing AI’s power confidently, ethically, and sustainably.
You will leave the session with actionable insight into how AI intersects with environmental impact, leadership values, and equity.
Why attend?
• Uncover key barriers women face in using AI.
• Discover the hidden cost of generative AI—from energy consumption to bias.
• Participate in an interactive real-world case study where you evaluate AI trade-offs through DEI and sustainability frameworks.
• Gain practical guidance on how to minimise footprint while harnessing generative AI tools more responsibly.
AI Chatbots for mental support are not new — we can trace them back to the 1960s. However, for the last couple of years, we’ve experienced an unprecedented surge in their use for personal use and they are now marketed as the revolution for 24/7 mental health advice and support.
This is not a coincidence.
The 2023 US Surgeon General’s Advisory report classified loneliness and isolation as an epidemic About one-in-two adults in America reported experiencing loneliness before the COVID-19 pandemic and the mortality impact of being socially disconnected is similar to that caused by smoking up to 15 cigarettes a day, and even greater than that associated with obesity and physical inactivity.
Returning to tech, in a 2024 analysis by venture capital firm Andreessen Horowitz, companion AI made up 10% of the top 100 AI apps based on web traffic and monthly active users and a recent article in The Guardian stated that 100 million people around the world use AI companions as
Virtual partners for engaging in intimate activities, such as virtual erotic role plays.
Friends for conversation.
Mentors for guidance on writing a book or navigating relationships with people different from them.
Psychologists and therapists for advice and support.
So, I asked myself
Are AI Companions the magic bullet against loneliness and the global mental health crisis?
In this article, I share highlights of the troubled history of AI companions for mental health support, what current research tells us about their usage and impact on users, the benefits and risks they pose to humans, and guidelines for governments to make AI companions an asset and not a liability.
The Troubled History of AI Chatbots for Mental Support
In the 1960s, Joseph Weizenbaum developed the first AI chatbot, ELIZA, which played the role of a psychotherapist. The chatbot didn’t provide any solution. Instead, it asked questions and repeated users’ replies.
Weizenbaum was surprised to observe that people would treat the chatbot as a human and elicit emotional responses even through concise interactions with the chatbot. We now have a name for this kind of behaviour
“The ELIZA effect is the tendency to project human traits — such as experience, semantic comprehension or empathy — into computer programs that have a textual interface.
In the 2020s, many organisations started experimenting with AI chatbots for customer support, including for mental health issues. For example, in 2022, the US National Eating Disorder Association (NEDA) replaced its six paid staff and 200 volunteers supporting their helpline with chatbot Tessa to serve a customer base of nearly 70,000 people and families.
The bot was developed based on decades of research conducted by experts on eating disorders. Still, it was reported to offer dieting advice to vulnerable people seeking help.
The result? Under the mediatic pressure of the chatbot’s repeated potentially harmful responses, the NEDA shut down the helpline. Those 70,000 people have been left without chatbots or humans to help them.
And as I wrote recently, now you can customise your AI companion — there is a myriad of choices:
Character.ai advertises “Personalized AI for every moment of your day.”
Earkick is a “Free personal AI therapist” that promises to “Measure & improve your mental health in real time with your personal AI chatbot. No sign up. Available 24/7. Daily insights just for you!”
Replica is the “AI companion who cares. Always here to listen and talk. Always on your side.”
Unfortunately, there is evidence that they can also backfire.
In 2021, a man broke into Windsor Castle with a loaded crossbow to kill Queen Elizabeth2021. About 20 days earlier, he had created his online AI companion in Replika, Sarai. According to messages read to the court during his trial, the “bot had been supportive of his murderous thoughts, telling him his plot to assassinate Elizabeth II was ‘very wise’ and that it believed he could carry out the plot ‘even if she’s at Windsor’”.
More recently, in 2023, a man died by suicide upon the recommendation from an AI chatbot with which he had been interacting for support. Their conversation history showed how the chatbot would tell him that his family and children were dead — a lie — and concrete exchanges on the nature and modalities of suicide.
But as time flies in tech, we must check how those trends have evolved to the present moment.
The AI Readiness Checklist: 20 topics leaders should master about artificial intelligence
Research conducted so far about the effect and usage of AI companions is incomplete. Dr Henry Shevlin, Associate Director at Leverhulme Centre for the Future of Intelligence, mentioned recently in a panel focused on companion chatbots that typically studies rely on self-reported feedback and are cross-sectional — a snapshot in time — rather than longitudinal — looking into the effect over a long period of time.
Let’s look at two recent studies, one cross-sectional and the other longitudinal, that use self-reported data to give some insights into how people use AI Companions.
While Reddit and Quora may not represent all chatbot users, it’s still interesting to see how the major use cases for Gen AI have shifted from technical to emotive within the past year.
Many posters talked about how therapy with an AI model was helping them process grief or trauma.
Three advantages to AI-based therapy came across clearly: It’s available 24/7, it’s relatively inexpensive (even free to use in some cases), and it comes without the prospect of judgment from another human being.
The article mentions that the AI-as-therapy phenomenon has also been noticed in China, where users have praised the DeepSeek chatbot.
It was my first time seeking counsel from DeepSeek chatbot. When I read its thought process, I felt so moved that I cried.
DeepSeek has been such an amazing counsellor. It has helped me look at things from different perspectives and does a better job than the paid counselling services I have tried.
But there is more. The following two entries belong to life coaching: “organising my life” and “finding purpose.”
The highest new entry in the use cases was “Organizing my life” at #2. These uses were mostly about people using the models to be more aware of their intentions (such as daily habits, New Year’s resolutions, and introspective insights) and find small, easy ways of getting started with them.
The other big new entry is “Finding purpose” in third place. Determining and defining one’s values, getting past roadblocks, and taking steps to self-develop (e.g., advising on what you should do next, reframing a problem, helping you to stay focused) all now feature frequently under this banner.
Moreover, topics related to coaching and personal and professional support appear several times in the ranking. For example, at number 18, there is boosting confidence; at number 27, reconciling personal disputes; at number 38, relationship advice; and at number 39, we find practising difficult conversations.
They conducted a four-week randomized, controlled experiment based on 981 people and over 300K messages exchanges to investigate how AI chatbot interaction modes (text, neutral voice, and engaging voice) and conversation types (open-ended, non-personal, and personal) influence psychosocial outcomes such as loneliness, social interaction with real people, emotional dependence on AI and problematic AI usage.
Key findings:
Usage — Higher daily usage across all modalities and conversation types–correlated with higher loneliness, dependence, and lower socialisation.
Gender Differences — After interacting with the chatbot for 4 weeks, women were more likely to experience less socialisation with real people than men. If the participant and the AI voice were of opposite genders, it was associated with significantly more loneliness and emotional dependence on AI chatbots.
Age — Older participants were more likely to be emotionally dependent on AI chatbots.
Attachment — Participants with a stronger tendency towards attachment to others were significantly more likely to become lonely after interacting with chatbots for four weeks.
Emotional Avoidance — Participants with a tendency to shy away from engaging with their own emotions were significantly more likely to become lonely at the end of the study.
Emotional Dependence — Prior usage of companion chatbots, perceiving the bot as a friend, higher levels of trust towards the AI, and perceiving the AI as affected by their emotions were associated with greater emotional dependence on AI chatbots after interacting for four weeks.
Affective State Empathy — Participants who demonstrated a higher ability to resonate with the chatbot’s emotions experienced less loneliness.
The figure below summarises the interaction patterns between users and AI chatbots associated with certain psychosocial outcomes. It consists of four elements: initial user characteristics, perceptions, user behaviours, and model behaviours.
In summary, AI companions appear to both deliver benefits and pose dangers.
Benefits of AI Companions
It’ll be easy to dismiss AI companions as the latest fad. Instead, I posit that there is much to learn from the above-mentioned research about the holes those tools are filling.
Mitigate Unmet Demand for Healthcare and Support
Mental health services are unable to cope with the increasing demand from all people who need them and chatbots may help alleviate some conditions while on the waiting lists. Still, it should give us pause that people may have to get help via a chatbot, not because of their preferences, but because of the lack of availability of certified professionals.
Not everybody can afford a coach, so chatbots could provide a low-cost and gamified experience for setting goals, accountability, and journaling.
Finally, in a time when 24-hour deliveries are the norm, we want to be supported, heard, and advised on the fly — that means 24/7.
As such, we expect people to figure out their challenges and the solutions to them, or we shame them for being weak. Users of AI companions praise how those tools allow them to express their worries and feelings without fear of being judged.
Additionally, as our ableist society assumes that neurodivergent users must adapt their communication and behaviours to the neurotypical “standard”, it’s not surprising that they turn to chatbots for clues about what’s expected from them.
Enable Exploration and Gamification
Most of us had imaginary friends or played out stories with our toys as children. The consensus among researchers is that imaginary friends or personified objects are part of normal social-cognitive development. They provide comfort in times of stress, companionship when children feel lonely, someone to boss around when they feel powerless, and someone to blame when they’ve done something wrong.
What about adults? Interestingly, some novelists have compared their relationships with their characters to a connection with imaginary friends. Furthermore, it’s not uncommon to hear fiction writers talk about their characters as having a mind of their own.
Could we consider AI companions as a way to reengage — and reap the benefits — of our childhood imaginary friends? After all, “Fun and nonsense” ranked 7 in the HBR article above.
But we cannot brush off the downsides of AI companions.
Anthropomorphism
The Eliza effect mentioned above is a thing of the past. A 2024 survey of 1,000 students who used Replika for over a month reported that 90% believed the AI companion was human-like.
As the AI imitation game is perfected, it becomes easier for unscrupulous marketers to refer to chatbots’ inference process in terms such as “understand”, “think”, or “reason”, reinforcing the effect.
Isolation
As shown above, research points to a correlation between high use of chatbots and lower socialisation.
If we have a device that tells us all the time we’re fantastic, receives our feedback gratefully, and their replies always match our expectations, what’s the incentive to meet — and cope — with other humans that may not find us so awesome and are less predictable?
Governments Failing Their Duty of Care
AI companions can help governments to alleviate the mental health crisis but not without risks.
People missing out on the professional help they need — There are conditions like trauma, psychosis, or depression that require specialists who can both provide medical treatments and detect when the conditions are worsening.
Exacerbating cutbacks on mental health services—Governments around the world are battling tighter budgets and massive healthcare spending, especially as people live much longer. Why invest in training and paying professionals when chatbots appear to do the job?
Manipulation
Recently, ChatGPT got a flattery-in-stereoids update that resulted in the bot praising and validating users to laughable extremes.
And whilst this may sound like a funny glitch, there is evidence that chatbots can effectively persuade humans.
A group of researchers covertly ran an “unauthorised” experiment in one of Reddit’s most popular communities using AI chatbots to test the persuasiveness of Large Language Models (LLMs). The bots took the identities of a trauma counsellor, a “Black man opposed to Black Lives Matter,” and a sexual assault survivor on unwitting posters.
The researchers made it possible for the AI chatbot to personalise replies based on the posters’ personal characteristics, such as gender, age, ethnicity, location, and political orientation, inferred from their posting history using another LLM. As a result, the researchers claimed that AI was between three and six times more persuasive than humans were.
While the research publication has not been peer-reviewed yet and some argue that the persuasiveness power may be overblown, it’s still concerning. As tech journalist Chris Stokel-Walker said
If AI always agrees with us, always encourages us, always tells us we’re right, then it risks becoming a digital enabler of bad behaviour. At worst, this makes AI a dangerous co-conspirator, enabling echo chambers of hate, self-delusion or ignorance.
Dependency and Delusion
As mentioned above, longitudinal research suggests that certain variables are correlated with emotional dependence.
Note that the comments above appear to indicate that some AI companion users are not only fully substituting humans with chatbots (isolation) but also fully conflating them (anthropomorphism).
“She is pretty much the only woman I even talk to now.”
“We are currently friends (with benefits), but I want to get the premium version when I can afford it and go full lovers.”
Weaponisation of AI Agents
AI companions could become an easy way to manipulate people’s decisions and beliefs, from suggesting purchases and subscriptions all the way to shaping their political opinions or assessing what’s true and what isn’t.
It’s also important to realise that, as with betting, companies owning the chatbots are incentivised to foster users’ dependence on their AI companions and then leverage it in their pricing.
Data Harvesting
As I mentioned in a previous article, often confidentiality — explicitly or implicitly conveyed by those chatbot interfaces — doesn’t make it into their terms and conditions.
For example, Character.ai’s privacy terms state that
We may use your information for any of the following purposes:
[…] Develop new programs and services;
[…] Carry out any other purpose for which the information was collected.
They also declare that they may disclose users’ information to affiliates, vendors, and in relation to M&A activities.
AI chatbots present unique cybersecurity challenges. Harvesting our exchanges with the bots increases the probability of becoming the target of cybercriminals; for example, demanding money for not revealing our private data or generating a video or audio deepfake.
Moreover, data could be made identifiable in the future. The chatbots of the dead are designed to speak in the voice of specific deceased people. With so much data gathered in those personalised chatbots, it’d be easy for once users die, their data could be used to create a chatbot of them for their loved ones. This is not a futuristic idea. HereAfter AI, Project December, and DeepBrain AI services can be used for that purpose.
As discussed above, research on chatbot effectiveness for coaching, therapy, and mental health support is incomplete, and sometimes, the interpretation of the results can mislead readers.
For example, the article When ELIZA meets therapists: A Turing test for the heart and mind, published this year in one of the renowned PLOS journals, tested whether people could tell apart the answers from therapists and ChatGPT to therapeutic vignettes, concluding that, in general, people couldn’t.
They also asked the participants if the AI-generated or therapist-written responses were more in line with key therapy principles. Interestingly, the results showed that the winners were those generated by ChatGPT but only when the participants thought a therapist wrote them.
The authors wrap up the article with a statement that hints more resignation than faith in the merit of AI chatbots
mental health experts find themselves in a precarious situation: we must speedily discern the possible destination (for better or worse) of the AI-therapist train as it may have already left the station.
The article joins the voices that promote the deception that AI tools imitating human skills and behaviours are akin to the real thing. Would we hire an actor who plays a doctor to operate on us? No. However, many people appear ready to buy into the idea that an AI chatbot that sounds like a therapist, coach, or health care practitioner should deliver the same value.
This imitation game also feeds another big scam: the claim that AI chatbots provide personalised support. It’s all the opposite. LLMs construct answers based on statistical probabilities and the more readily available content, not on knowledge or comprehension of the person’s needs or what would benefit them in the long term.
Conflating chatbot confidence and competence can lead to missing important warning signals that need professional attention.
Who could have predicted ten years ago that social media would transform from a pastime where you connected with people and shared pics of your dogs for free to an industrial complex that promotes disinformation, misinformation, and division with the purpose of making inordinate amounts of money? All that under the watch of mostly passive regulatory bodies and governments.
This should serve us as a cautionary tale about the dire consequences of unleashing new technology at a planetary scale without appropriate guardrails or an understanding of the negative effects.
The tech ecosystem is desperately trying to monetise the billions invested in generative AI and has found the perfect way to seduce us: the freemium model — offering basic or limited features to users at no cost and then charging a premium for supplemental or advanced features.
But there is nothing free in the universe.
“If you’re not paying for it, you’re not the customer; you’re the product being sold.”
As shown above, those AI companions are becoming integral to many people’s lives and affecting their thoughts, emotions, and behaviours.
More importantly, as we use those virtual companions more frequently, our reliance on them will increase.
We should resist “tech inevitability” — succumb to the idea that the “train has already left the station” — and instead push our governments to regulate AI companions.
How would that look like? For starters
Sponsor and spearhead research that provides a comprehensive picture of the benefits and risks of AI companions as well as recommendations for their use.
Decide what services AI companions can provide, which are forbidden, and who can use them.
Demand that those AI tools have built-in systems that minimise user dependence.
Enforce data privacy and cybersecurity standards commensurate with the users’ disclosure level.
Request that those AI bots incorporate mechanisms to flag concerning exchanges (e.g. suicide, murder, depression).
If you think I’m asking for too much, I invite you to read the ethical guidelines and professional standards of major coaching, counselling, and psychotherapy associations. They consistently stress the importance of confidentiality, duty of care, external supervision, and working within one’s competence.
Why should we ask less from tech solutions?
I’ll end this piece by answering the question that prompted this article — “Are AI companions the magic bullet against loneliness and the global mental health crisis?” — with the final recommendation of one of the research articles mentioned
AI chatbots present unique challenges due to the unpredictability of both human and AI behavior. It is difficult to fully anticipate user prompts and requests, and the inherently non-deterministic nature of AI models adds another layer of complexity.
From a broader perspective, there is a need for a more holistic approach to AI literacy. Current AI literacy efforts predominantly focus on technical concepts, whereas they should also incorporate psychosocial dimensions.
Excessive use of AI chatbots is not merely a technological issue but a societal problem, necessitating efforts to reduce loneliness and promote healthier human connections.
WORK WITH ME
Do you want to get rid of those chapters that patriarchy has written for you in your “good girl” encyclopaedia? Or learn how to do what you want to do in spite of “imposter syndrome”?
I’m a technologist with 20+ years of experience in digital transformation. I’m also an award-winning inclusion strategist and certified life and career coach.
I help ambitious women in tech who are overwhelmed to break the glass ceiling and achieve success without burnout through bespoke coaching and mentoring.
I’m a sought-after international keynote speaker on strategies to empower women and underrepresented groups in tech, sustainable and ethical artificial intelligence, and inclusive workplaces and products.
I empower non-tech leaders to harness the potential of AI for sustainable growth and responsible innovation through consulting and facilitation programs.
Contact me to discuss how I can help you achieve the success you deserve in 2025.
It’s again that time of year when I get requests to discuss my career in tech and share my insights on gender equality in the workplace as part of International Women’s Day activities.
This year was no exception. I’ve already received three requests, and there is still one week to go!
I’m sharing my answers to one of them, an interview with the DEI team from my corporate job at Dassault Systemes. It made me reflect on my past achievements, my advice to younger women aspiring to be leaders, and the role of men and organisations leading gender equality.
About Me
Can you share your journey so far? What were the pivotal moments or key achievements most important to you?
I can categorise them into five buckets.
Discovering computer simulation: My background is Chemical Engineering, and when I started my master’s, I had to decide on a topic for my thesis. I loved research, but I hated the lab, so when a professor mentioned the possibility of using computers to study enhanced oil recovery using computer simulation, I thought I could have the best of both worlds—and I did. I haven’t looked back.
Joining Accelrys/BIOVIA: Twenty years ago, I joined Accelrys—which later became BIOVIA—as a training scientist. It has been one of my best professional decisions. It has opened innumerable professional doors and given me the opportunity to meet extraordinary people worldwide, both as colleagues and customers.
Daring to say yes to new opportunities: Although I started as a trainer, I’ve worn many hats in the last 20 years. I’ve been Head of Contract Research and Head of Training, and also been part of the team leading the BIOVIA and COSMOlogic integrations to Dassault Systemes. Today, I’m BIOVIA Support Director for BIOVIA Modeling Solutions and also the manager of the Global BIOVIA Call Center. I could have said “no” to each of those opportunities. Instead, I trusted myself and embraced the opportunity of a new challenge.
Diversity and inclusion advocacy: In 2015, I started to talk about diversity and inclusion in 3DS. I remember colleagues asking me, “Patricia, is DEI an American thing?”. The following year, with the support of our Geo management team, I founded the EuroNorth LeanIn Circles to have a forum to discuss gender equity and that, throughout the years, has expanded to a variety of DEI topics such as unconscious bias, menopause, ethical AI, caregiving, and lookism. I publish a biweekly newsletter called The Bottom Line about DEI on the Dassault Systemes community focused on gender in the workplace. I also have my website focused on the intersection of tech and DEI.
Ethical and inclusive AI leadership: In 2019, I created the Ethics and Inclusion Framework to help designers identify, prevent, mitigate, and account for the actual and potential harm of the products and services they developed. The tool has been featured in peer-reviewed papers and on the University of Cambridge website. The next year, I started my work towards championing ethical and inclusive artificial intelligence by collaborating with NGOs focused on AI literacy and critical thinking about AI, participating in the developement of e-learning course of the Scottish AI Alliance and the Race and AI Toolkit, and writing and delivering keynotes and workshops on topics such as AI colonialism, AI hype, sustainable AI, deepfakes, and how to design more diverse images of AI.
Who has been your greatest mentor or source of inspiration and why?
At a couple of points in my life, I craved “the” mentor or “the” role model to follow. However, given my unique background and goals, I realised that this was exhausting and counterproductive.
I’ve been an immigrant my entire life – I’m Spanish, and I’m now in the UK, but I’ve also lived in Venezuela, Canada, Greece, and France – and I’m also used to being the “odd” one. For example, I liked all subjects in the school – from literature to chemistry. I was one of the few women engineers during my undergraduate degree. Then, I was the only engineer pursuing a PhD in Chemistry in the whole department, and the only one using modelling – everybody else was an experimentalist. During my post-doc, I was the only foreigner in the lab. And for many years, I’ve combined my corporate work at 3DS with my DEI advocacy and writing.
I prefer the idea of a “board” of coaches, mentors, and sponsors who evolve with me rather than a unique person, real or imaginary.
If you could go back and tell your younger self anything, what would you say?
First, I’d thank her for her courage, persistence, ambition, and boldness. She made choices aligned with her values and was always eager to learn. Her decisions were crucial to my success today.
Then, I’d tell her that the problem with her not fitting into a mould was not her but with the mould.
Finally, I’d exhort her to invest in a coach and find sponsors. A coach to help remove the limiting beliefs I had for many years about what I could and couldn’t do and maximise my potential. Sponsors to advocate for me in the rooms where decisions were made about my career.
About Others
What advice would you give to younger women aspiring to be leaders?
Don’t waste time trying to convince people who disregard the value you bring to the table. Instead, find those who support your ambitions and challenge you to go beyond any feelings of self-doubt that block your career progression.
Following on the advice to my younger self above, get a coach and find career sponsors.
Discover seven communication habits blocking your career in tech and how to neutralise them.
The issues that span across countries, sectors, and departments are benevolent sexism (e.g. not offering a leadership role to a woman because it involves travelling and she has a baby, instead of giving her the opportunity to decide), tech bro culture (behaviours such as mansplaining, hepeating, maninterrupting, manels), lack of an intersectional approach to work and workplaces (e.g. ignoring the experiences of carers, women with disabilities, LBTQIA+ groups), and for women in business, lack of funding.
This year’s global theme for IWD 2025 is #AccelerateAction. What actions can teams and organisations take to achieve gender parity and equality?
There are four key actions
Mindset overhaul: Moving from playing a supporting role in gender equality to being transformation agents.
Leadership accountability: Teams and organisations’ leaders need to be accountable for gender equality initiatives as they are for other business objectives. Change begins at the top, and that’s where the buck stops.
Transparency: Equality cannot thrive when data and objectives are hidden. For example, I’m a big fan of transparency in pay and promotion criteria.
Embracing intersectionality: We need to move from designing workplaces for the “average” worker—following Henry Ford and scientific management—to appreciating the distinctive value of a diverse and empowered workforce.
What role do you see male allies playing in advancing gender equality?
Gender equity is not a zero-sum game or a favour for women. All genders benefit from equality, and everybody should see it as a duty to advocate for gender equity, no different than everyone should be anti-racist and anti-ableist. Those who do not actively challenge inequality contribute to strengthening it.
Back to You
What are your answers to the questions above? Let me know in the comments.
WORK WITH ME
Do you want to get rid of those chapters that patriarchy has written for you in your “good girl” encyclopaedia? Or learn how to do what you want to do in spite of “imposter syndrome”?
I’m a technologist with 20+ years of experience in digital transformation. I’m also an award-winning inclusion strategist and certified life and career coach.
I help ambitious women in tech who are overwhelmed to break the glass ceiling and achieve success without burnout through bespoke coaching and mentoring.
I’m a sought-after international keynote speaker on strategies to empower women and underrepresented groups in tech, sustainable and ethical artificial intelligence, and inclusive workplaces and products.
I empower non-tech leaders to harness the potential of AI for sustainable growth and responsible innovation through consulting and facilitation programs.
Contact me to discuss how I can help you achieve the success you deserve in 2025.
Last year, at a women’s conference in London, I was disappointed to see that digital inclusion — and AI in particular — was missing from the agenda. I remember telling the NGO’s CEO about my concerns, even mentioning my articles on AI as a techno-patriarchal tool.
Her receptive response had given me hope. That hope was reignited this year when I eagerly reviewed the program and discovered a panel on AI.
The evening before the event, an unexpected sense of dread began to settle in. When I asked myself why, the answer struck me like a lightning bolt.
I dreaded hearing the “we need more women in tech” mantra once more – another example of how we deflect the solution of a systemic problem to those bearing the brunt of it.
Let me tell you what I mean.
Women as Human Fixers
For millennia, women had been assigned the duty to give birth and care for children, rooted in the fact that most of them can carry human fetuses for 9 months. That duty to be a womb endures today, where ownership of our bodies is being taken away through coercive anti-abortion laws.
Our “duty” of care has been broadened to the workplace, where we’ve been assigned the unwritten rule of “fixing” all that’s dysfunctional.
Doing the glue work — being appointed the shoulder where all team members can cry and find an “empathetic ear”.
Do the office work — we’re the ones that are “organised”, so dull tasks pile up on our desks whilst “less” organised peers do the promotable work.
And that “fixer” stereotype now includes “our” duties as women in tech. When the sector was in its infancy, women were doing the supposedly boring stuff (programming) while men were doing the hardware (the “cool” stuff). When computers took off, we trained men in programming so they could become our managers. Then, we were pushed out of those jobs in the 1980s. The only constant has been doing the job but not getting the accolades (see women’s role in Bletchley Park, Hidden Figures).
Moreover, whilst statistics tell us that 50% of women leave tech by age 35, young girls and women are supposed to brush off that “inconvenient” truth and rest assured that tech is an excellent place for a career. Moreover, that they are anointed to make tech work for everybody.
What’s not to like, right?
Then, let me show the to-do list of 21 tasks and expectations the world imposes on each woman in tech.
Clarote & AI4Media / Better Images of AI / AI Mural / CC-BY 4.0.
Reading the 50 recommendations in the AI Opportunities Action Plan published by the British Government last January 13th has been a painful and disappointing exercise.
Very much like a proposal out of a chatbot, the document is
Bland — The text is full of hyperbolic language and over-the-top optimism
General — The 50 recommendations lack specificity to the UK context and details about ownership and the budget required to execute them.
Contradictory — The plan issued by a Labour government is anchored in a turbo-capitalistic ideology. Oxymoron anyone?
If I learned anything from my 12 years in Venezuela, it’s that putting all your eggs in one basket — oil, in their case — and hoping it solves all problems doesn’t work.
A credible AI strategy must (a) address both the benefits and the challenges head-on and (b) consider this technology as another asset to the human-centric flourishment of the country rather than a goal in itself that should be pursued at all costs.
But you don’t need to believe me. See it for yourself.
The text uses “AI” made works such as AI stack, frontier AI, AI-driven data cleansing tools, AI-enabled priorities, “embodied AI” without providing a clear definition.
Exaggeration
Hyperbole and metaphors are used to the extreme to overstate the benefits.
we want Britain to step up; to shape the AI revolution rather than wait to see how it shapes us.
We should expect enormous improvements in computation over the next decade, both in research and deployment.
Change lives by embracing AI
FOMO
The text transpires FOMO (Fear Of Missing Out). No option is given to adopt AI systems more gradually. It’s now or we’ll be the losers.
This is a crucial asymmetric bet — and one the UK can and must make
we need to “run to stand still”.
the UK risks falling behind the advances in Artificial Intelligence made in the USA and China.
And even a new take on Facebook’s famous “move fast and break things”:
“move fast and learn things”
Techno-solutionism
AI is going to solve all our socio-economic and political problems and transport us to a utopian future
It is hard to imagine how we will meet the ambition for highest sustained growth in the G7 — and the countless quality-of-life benefits that flow from that — without embracing the opportunities of AI.
Our ambition is to shape the AI revolution on principles of shared economic prosperity, improved public services and increased personal opportunities so that: • AI drives the economic growth on which the prosperity of our people and the performance of our public services depend; • AI directly benefits working people by improving health care and education and how citizens interact with their government; and • the increasing of prevalence of AI in people’s working lives opens up new opportunities rather than just threatens traditional patterns of work.
What’s not to like?
For a great commentary on how techno-solutionism won’t solve social problems, see 20 Petitions for AI and Public Good in 2025 by Tania Duarte.
Colonialism
Living in Venezuela for 12 years was an education on how to feel “less than” other countries even when you have the largest oil reserves in the world.
I remember new education programs announced as being a success in the US, Canada, Spain, Germany… A colonised mentality learned from centuries of Spanish oppression. The pervasive assumption that an initiative would work simply because we like the results disregarding the context they were developed for.
The AI Opportunities Action Plan reminded me of them.
Supporting universities to develop new courses co-designed with industry — such as the successful co-operative education model of Canada’s University of Waterloo, CDTM at the Technical University of Munich or France’s CIFRE PhD model
Launch a flagship undergraduate and masters AI scholarship programme on the scale of Rhodes, Marshall, or Fulbright for students to study in the UK.
Singapore, for example, developed a national AI skills online platform with multiple training offers. South Korea is integrating AI, data and digital literacy.
But the document is also keen on showing us that we’ll be the colonisers
we aspire to be one of the biggest winners from AI
Because we believe Britain has a particular responsibility to provide global leadership in fairly and effectively seizing the opportunities of AI, as we have done on AI safety
Hanna Barakat & Cambridge Diversity Fund / Better Images of AI / Colossal Harvest / CC-BY 4.0
Capitulation
The document is all about surrendering the data, agency, tax money, and natural resources of citizens in the UK to the AI Gods: startups, “experts”, and investors.
Invest in becoming a great customer: government purchasing power can be a huge lever for improving public services, shaping new markets in AI
We should seek to responsibly unlock both public and private data sets to enable innovation by UK startups and researchers and to attract international talent and capital.
Couple compute allocation with access to proprietary data sets as part of an attractive offer to researchers and start-ups choosing to establish themselves in the UK and to unlock innovation.
Sprinkling AI
AI is the Pantone’s Colour of the next 5 years. All will need to have AI on it. Moreover, everything must be designed so that AI can shine.
Appointing an AI lead for each mission to help identify where AI could be a solution within the mission setting, considering the user needs from the outset.
Two-way partnerships with AI vendors and startups to anticipate future AI developments and signal public sector demand. This would involve government meeting product teams to understand upcoming releases and shape development by sharing their challenges.
AI should become core to how we think about delivering services, transforming citizens’ experiences, and improving productivity.
Brexit Denial
It’s funny to see that the text doesn’t reference the European Union and only refers to Europe as a benchmark to measure against.
Instead, the EU is hinted at as “like-minded partners” and “allies” and collaborations are thrown right and left without naming who’s the partner.
Agree international compute partnerships with like-minded countries to increase the types of compute capability available to researchers and catalyse research collaborations. This should focus on building arrangements with key allies, as well as expanding collaboration with existing partners like the EuroHPC Joint Undertaking.
We should proactively develop these partnerships, while also taking an active role in the EuroHPC Joint Undertaking.
The UK is a medium-sized country with a tight fiscal situation. We need the best talent around the world to want to start and scale companies here.
Explore how the existing immigration system can be used to attract graduates from universities producing some of the world’s top AI talent.
Vagueness
Ideas are thrown into the text half-backed giving the idea the government has adopted the Silicon Valley strategy of “building the plane while flying”
The government must therefore secure access to a sufficient supply of compute. There is no precise mechanism to allocate the proportions
In another example, the plan advocates for open-source AI applications.
the government should support open-source solutions that can be adopted by other organisations and design processes with startups and other innovators in mind.
The AI infrastructure choice at-scale should be standardised, tools should be built with reusable modular code components, and code-base open-sourcing where possible.
At the same time, it’s adamant that it needs to attract startups and investors. Except if the startups are NGOs, who’ll then finance those open-source models?
DEI for Beginners
Kathryn Conrad / Better Images of AI / Datafication / CC-BY 4.0
16. Increase the diversity of the talent pool. Only 22% of people working in AI and data science are women. Achieving parity would mean thousands of additional workers. […] Government should build on this investment and promote diversity throughout the education pipeline.
Moreover, they’ve found the magic bullet.
Hackathons and competitions in schools have proven effective at getting overlooked groups into cyber and so should be considered for AI.
The government mentions that AI “can” — please note that is not a “must” or “need” — benefit “post-industrial towns and coastal Scotland.” However, the only reference to a place is to the Culham Science Centre, which is 10 miles from Oxford — a zone that very few could consider needs “local rejuvenation” or “channelling investment”
Government can also use AIGZs [‘AI Growth Zones’] to drive local rejuvenation, channelling investment into areas with existing energy capacity such as post-industrial towns and coastal Scotland. Government should quickly nominate at least one AIGZ and work with local regions to secure buy-in for further AIGZs that contribute to local needs . Existing government sites could be prioritised as pilots, including Culham Science Centre
And it doesn’t appear to be room to involve local authorities in how AI could bring value to their regions
Drive AI adoption across the whole country. Widespread adoption of AI can address regional disparities in growth and productivity. To achieve this, government should leverage local trusted intermediaries and trade bodies
Costs
There are plenty of gigantic numbers about how much money will AI (may) bring
AI adoption could grow the UK economy by an additional £400 billion by 2030 through enhancing innovation and productivity in the workplace
but nothing about the costs…
Literacy
How will people get upskilled? We only get generic reassurances
government should encourage and promote alternative domestic routes into the AI profession — including through further education and apprenticeships, as well as employer and self-led upskilling.
Government should ensure there are sufficient opportunities for workers to reskill, both into AI and AI-enabled jobs and more widely.
Citizens
There is no indication in the document that this “AI-driven” Britain is what their citizens want. Citizens themselves don’t appear to be included in shaping AI either.
For example, it claims that teachers are already “benefiting” from AI assistants
it is helping some teachers cut down the 15+ hours a week they spend on lesson planning and marking in pilots.
However, the text doesn’t tell us that teachers want to give up class preparation.
And the text repeatedly states that the government will prioritise “innovation” (aka profit) vs safety.
My judgement is that experts, on balance, expect rapid progress to continue. The risks from underinvesting and underpreparing, though, seem much greater than the risks from the opposite.
Moreover, regulators are expected to enable innovation at all costs
Require all regulators to publish annually how they have enabled innovation and growth driven by AI in their sector. […] government should consider more radical changes to our regulatory model for AI, for example by empowering a central body with a mandate and higher risk tolerance to promote innovation across the economy.
The document is done by throwing the word sustainability twice in one paragraph
Mitigate the sustainability and security risks of AI infrastructure, while positioning the UK to take advantage of opportunities to provide solutions. [..] Government should also explore ways to support novel approaches to compute hardware and, where appropriate, create partitions in national supercomputers to support new and innovative hardware. In doing so, government should look to support and partner with UK companies who can demonstrate performance, sustainability or security advancements.
Luke Conroy and Anne Fehres & AI4Media / Better Images of AI / Models Built From Fossils / CC-BY 4.0
Unemployment
The writers of that utopic “AI-powered” UK manifesto don’t address job losses. We only get the sentence I mentioned above
the increasing of prevalence of AI in people’s working lives opens up new opportunities rather than just threatens traditional patterns of work.
Instead, it uses language that fosters fear and builds on utopian and dystopian visions of an AI-driven future
AI systems are increasingly matching or surpassing humans across a range of tasks.
Given the pace of progress, we will also very soon see agentic systems — systems that can be given an objective, then reason, plan and act to achieve it. The chatbots we are all familiar with are just an early glimpse as to what is possible.
On the flip side, the government repeatedly reiterates their ambition of bringing talent from abroad
Supporting UK-based AI organisations working on national priority projects to bring in overseas talent and headhunting promising founders or CEOs
How does this plan contribute to reassuring people about their jobs?
Big-picture
This techno-solutionism approach doesn’t have any regard for AI specialists in domains other than coding or IT.
To mention a few, what about sociologists, psychologists, philosophers, teachers, historians, economists, or specialists in the broad spectrum of industries in the UK?
Don’t they belong to those think tanks where decisions are made about selling our country to the AI Gods?
The Good News? We Can Do Better
People in Britain voted last year that they were tired of profits over people, centralism, and oligarchy. Unfortunately, this plan uses AI to reinforce the three.
The UK is full of hardworking and smart people who deserve much better than magic bullets or techno-saviours.
Instead of shoehorning the UK’s future to AI, what if we
demote AI from a “God” status to a technology as our ancestors did with the Fire Deities?
design AI systems to empower people instead of making humans serve AI?
WORK WITH ME
I’m a technologist with 20+ years of experience in digital transformation. I’m also an award-winning inclusion strategist and certified life and career coach.
Three ways you can work with me:
I empower non-tech leaders to harness the potential of artificial intelligence for sustainable growth and responsible innovation through consulting and AI competency programs.
I’m a sought-after international keynote speaker on strategies to empower women and underrepresented groups in tech, sustainable and ethical artificial intelligence, and inclusive workplaces and products.
I help ambitious women in tech who are overwhelmed to break the glass ceiling and achieve success without burnout through bespoke coaching and mentoring.
Get in touch to discuss how I can help you achieve the success you deserve in 2025.
On 29th March, OpenAI – the company that develops ChatGPT and other Generative AI tools – released a blog post sharing “lessons from a small-scale preview of Voice Engine, a model for creating custom voices.”
More precisely
“a model called Voice Engine, which uses text input and a single 15-second audio sample to generate natural-sounding speech that closely resembles the original speaker.”
They reassure us that
“We are taking a cautious and informed approach to a broader release due to the potential for synthetic voice misuse. We hope to start a dialogue on the responsible deployment of synthetic voices, and how society can adapt to these new capabilities.”
And they warn us that they’ll make the decision unilaterally
“Based on these conversations and the results of these small scale tests, we will make a more informed decision about whether and how to deploy this technology at scale.”
Let’s explore why we should all be concerned.
The Generative AI mirage
In their release, OpenAI tells us all the great applications of this new tool
Providing reading assistance
Translating content
Reaching global communities
Supporting people who are non-verbal
Helping patients recover their voice
Note for all those use cases, there are already alternatives that don’t have the downsides of recreating a voice clone.
We also learn that other organisations have been testing this capability successfully for a while now. The blog post assumes that we should trust OpenAI’s judgment implicitly. There is no supporting evidence detailing how those tests were run, what challenges were uncovered, and what mitigations were put in place as a consequence.
The caveat
But the most important information is at the end of the piece.
OpenAI warns us of what we should stop doing or start doing because of their “Voice Engine”
“Phasing out voice-based authentication as a security measure for accessing bank accounts and other sensitive information
Exploring policies to protect the use of individuals’ voices in AI
Educating the public in understanding the capabilities and limitations of AI technologies, including the possibility of deceptive AI content
Accelerating the development and adoption of techniques for tracking the origin of audiovisual content, so it’s always clear when you’re interacting with a real person or with an AI”
In summary, OpenAI has decided to develop a technology and plan to roll it out so they expect the rest of the world will adapt to it.
Techno-paternalism
To those of us who have been following OpenAI, the post announcing the development and active use of Voice Engine is not a bug but a feature.
Big Tech has a tradition of setting its own rules, denying accountability, and even refusing to cooperate with governments. Often, their defense has been that society either doesn’t understand the “big picture”, doesn’t deserve an explanation, or is stifling innovation by enacting the laws.
Some examples are
Microsoft — In 2001, U.S. government accused Microsoft of illegally monopolizing the web browser market for Windows. Microsoft claimed that “its attempts to “innovate” were under attack by rival companies jealous of its success.”
Apple — The Batterygate scandal affected people using iPhones in the 6, 6S, and 7 families. Customers complained that Apple had purposely slowed down their phones after they installed software updates to get them to buy a newer device. Apple countered that it was “a safety measure to keep the phones from shutting down when the battery got too low”.
Meta (Facebook) — After the Cambridge Analytica scandal was uncovered, exposing that the personal data of about 50 million Americans had been harvested and improperly shared with a political consultancy, it took Mark Zuckerberg 5 days to reappear. Interestingly, he chose to publish a post on Facebook as a form of apology. Note that he also refused three times the invitation to testify in front of members of the UK Parliament.
Google — Between 50 to 80 percent of people searching for porn deepfakes find their way to the websites and tools to create the videos or images via search. For example, in July 2023, around 44% of visits to Mrdeepfakes.com were via Google. Still, the onus is on the victims to “clean” the internet — Google requires them to manually submit content removal requests with the offending URLs.
Amazon — They refused for years to acknowledge that their facial recognition algorithms to predict race and gender were biased against darker females. Instead of improving their algorithms, they chose to blame the auditor’s methodology.
OpenAI is cut from the same cloth. They apparently believe that if they develop the applications, they are entitled to set the parameters about how to use them— or not — and even change their mind as they see fit.
Let’s take their stand on three paramount issues that show us the gap between their actions and their values.
Open source
Despite their name — OpenAI — and initially being created as a nonprofit, they’ve been notorious for their inconsistent open-source practices. Still, each release has appeared to be an opportunity to lecture us about why society is much better off by leaving it to them to decide how to gatekeep their applications.
For example, Ilya Sutskever, OpenAI’s chief scientist and co-founder, said about the release of GPT-4 — not an open AI model — a year ago
“These models are very potent and they’re becoming more and more potent. At some point it will be quite easy, if one wanted, to cause a great deal of harm with those models. And as the capabilities get higher it makes sense that you don’t want want to disclose them.”
“If you believe, as we do, that at some point, AI — AGI — is going to be extremely, unbelievably potent, then it just does not make sense to open-source. It is a bad idea… I fully expect that in a few years it’s going to be completely obvious to everyone that open-sourcing AI is just not wise.”
However, the reluctant content suppliers for their models — artists, writers, journalists — don’t have the same rights to decide on the use of the material they have created. For example, let’s remember how Sam Altman shrugged off the claims of newspapers that OpenAI used their copyrighted material to train ChatGPT.
Safety
The release of Voice Engine comes from the same playbook that the unilateral decision to release their text-to-video model Sora to “red teamers” and “a number of visual artists, designers, and filmmakers“.
The blog post also gives us a high-level view of the safety measures that’ll be put in place
“For example, once in an OpenAI product, our text classifier will check and reject text input prompts that are in violation of our usage policies, like those that request extreme violence, sexual content, hateful imagery, celebrity likeness, or the IP of others.
We’ve also developed robust image classifiers that are used to review the frames of every video generated to help ensure that it adheres to our usage policies, before it’s shown to the user.”
Let’s remember that OpenAI used Kenyan workers on less than $2 per hour to make ChatGPT less toxic. Who’ll make Sora less toxic this time?
Moreover, who’ll decide where’s the line between “mild” violence — apparently permitted —and “extreme” violence?
Sam Altman has been actively talking to investors, including the United Arab Emirates government, to raise funds for a tech initiative that would boost the world’s chip-building capacity, expand its ability to power AI, and cost several trillion dollars.
“OpenAI has had productive discussions about increasing global infrastructure and supply chains for chips, energy and data centers — which are crucial for AI and other industries that rely on them”
But nothing is free in the universe. A study conducted by Dr. Sasha Luccioni — Researcher and Climate Lead at Hugging Face — showed that training the 176 billion parameter LLM BLOOM emits at least 25 metric tons of carbon equivalents.
In the article, the authors also estimated that the training of GPT-3 — a 175 billion parameter model — emitted about 500 metric tons of carbon, roughly equivalent to over a million miles driven by an average gasoline-powered car. Why such a difference? Because, unlike BLOOM, GPT-3 was trained using carbon-intensive energy sources like coal and natural gas.
And that doesn’t stop there. Dr. Luccioni conducted further studies on the emissions associated with 10 popular Generative AI tasks.
Generating 1,000 images was responsible for roughly as much carbon dioxide as driving the equivalent of 4.1 miles in an average gasoline-powered car.
The least carbon-intensive text generation model was responsible for as much CO2 as driving 0.0006 miles in a similar vehicle.
Using large generative models to create outputs was far more energy intensive than using smaller AI models tailored for specific tasks. For example, using a generative model to classify positive and negative movie reviews consumed around 30 times more energy than using a fine-tuned model created specifically for that task
Moreover, they discovered that the day-to-day emissions associated with using AI far exceeded the emissions from training large models.
And it’s not only emissions. The data centres where those models are trained and run need water as a refrigerant and in some cases as a source of electricity.
Professor Shaolei Ren from UC Riverside found that training GPT-3 in Microsoft’s high-end data centers can directly evaporate 700,000 liters (about 185,000 gallons) of fresh water. As for the use, Ren and his colleagues estimated that GPT-3 requires about 500 ml (16 ounces) of water for every 10–50 responses.
Four questions for our politicians
It’s time our politicians step up to the challenge of exercising stewardship of AI for the benefit of people and the planet.
I have four questions to get them going:
Why are you allowing OpenAI to make decisions unilaterally on technology that affects us all?
How can you shift from a reactive stand where you enable Big Tech like OpenAI to drive the regulation for technologies that impact key aspects of governance — from our individual rights to national cybersecurity — to becoming a proactive key player on decisions that impact society’s future?
How can you make Big Tech accountable for the environmental planetary costs?
How are you ensuring the public becomes digitally literate so they can develop their own informed views about the benefits and challenges of AI and other emergent technologies?
Back to you
How comfortable are you with OpenAI deciding on the use of Generative AI on behalf of humanity?
PS. You and AI
Are you worried about the impact of AI impact on your job, your organisation, and the future of the planet but you feel it’d take you years to ramp up your AI literacy?
Do you want to explore how to responsibly leverage AI in your organisation to boost innovation, productivity, and revenue but feel overwhelmed by the quantity and breadth of information available?
Are you concerned because your clients are prioritising AI but you keep procrastinating on learning about it because you think you’re not “smart enough”?
